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Data Mining and Predictive Analysis
 
 

Data Mining and Predictive Analysis, 1st Edition

Intelligence Gathering and Crime Analysis

 
Data Mining and Predictive Analysis, 1st Edition,ISBN9780750677967
 
 
 

Butterworth-Heinemann

9780750677967 New edition

9780080464626

368

235 X 191

Cuts through the technical language of other books to provide an ideal primer for those looking to use data mining in crime and intelligence analysis

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Key Features

* Serves as a valuable reference tool for both the student and the law enforcement professional
* Contains practical information used in real-life law enforcement situations
* Approach is very user-friendly, conveying sophisticated analyses in practical terms

Description

It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. In this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity.

Knowledge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis. The book is a starting point for those thinking about using data mining in a law enforcement setting. It provides terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis, which law enforcement and intelligence professionals can tailor to their own unique situation and responsibilities.

Readership

Government agencies and institutions, law enforcement agencies (crime analysts and criminal investigators). Managers and command staff making data mining purchasing decisions, data mining and artificial intelligence developers, private security consultants, legislators, and policy makers.

Data Mining and Predictive Analysis, 1st Edition

  • Dedication
  • Foreword
  • Preface
  • Introduction
  • Introductory Section
  • Chapter 1: Basics
    • 1.1 Basic Statistics
    • 1.2 Inferential versus Descriptive Statistics and Data Mining
    • 1.3 Population versus Samples
    • 1.4 Modeling
    • 1.5 Errors
    • 1.6 Overfitting the Model
    • 1.7 Generalizability versus Accuracy
    • 1.8 Input/Output
  • Chapter 2: Domain Expertise
    • 2.1 Domain Expertise
    • 2.2 Domain Expertise for Analysts
    • 2.3 Compromise
    • 2.4 Analyze Your Own Data
  • Chapter 3: Data Mining
    • 3.1 Discovery and Prediction
    • 3.2 Confirmation and Discovery
    • 3.3 Surprise
    • 3.4 Characterization
    • 3.5 “Volume Challenge”6
    • 3.6 Exploratory Graphics and Data Exploration
    • 3.7 Link Analysis12
    • 3.8 Nonobvious Relationship Analysis (NORA)13
    • 3.9 Text Mining
    • 3.10 Future Trends
    • Methods
  • Chapter 4: Process Models for Data Mining and Analysis
    • 4.1 CIA Intelligence Process
    • 4.2 CRISP-DM
    • 4.3 Actionable Mining and Predictive Analysis for Public Safety and Security
  • Chapter 5: Data
    • 5.1 Getting Started
    • 5.2 Types of Data
    • 5.3 Data2
    • 5.4 Types of Data Resources
    • 5.5 Data Challenges
    • 5.6 How Do We Overcome These Potential Barriers?
    • 5.7 Duplication
    • 5.8 Merging Data Resources
    • 5.9 Public Health Data
    • 5.10 Weather and Crime Data
  • Chapter 6: Operationally Relevant Preprocessing
    • 6.1 Operationally Relevant Recoding
    • 6.2 Trinity Sight
    • 6.3 Duplication
    • 6.4 Data Imputation
    • 6.5 Telephone Data
    • 6.6 Conference Call Example
    • 6.7 Internet Data
    • 6.8 Operationally Relevant Variable Selection
  • Chapter 7: Predictive Analytics
    • 7.1 How to Select a Modeling Algorithm, Part I
    • 7.2 Generalizability versus Accuracy
    • 7.3 Link Analysis
    • 7.4 Supervised versus Unsupervised Learning Techniques2
    • 7.5 Discriminant Analysis
    • 7.6 Unsupervised Learning Algorithms
    • 7.7 Neural Networks
    • 7.8 Kohonan Network Models
    • 7.9 How to Select a Modeling Algorithm, Part II
    • 7.10 Combining Algorithms
    • 7.11 Anomaly Detection
    • 7.12 Internal Norms
    • 7.13 Defining “Normal”
    • 7.14 Deviations from Normal Patterns
    • 7.15 Deviations from Normal Behavior
    • 7.16 Warning! Screening versus Diagnostic
    • 7.17 A Perfect World Scenario
    • 7.18 Tools of the Trade
    • 7.19 General Considerations and Some Expert Options
    • 7.20 Variable Entry
    • 7.21 Prior Probabilities
    • 7.22 Costs
  • Chapter 8: Public Safety-Specific Evaluation
    • 8.1 Outcome Measures
    • 8.2 Think Big
    • 8.3 Training and Test Samples
    • 8.4 Evaluating the Model
    • 8.5 Updating or Refreshing the Model
    • 8.6 Caveat Emptor
  • Chapter 9: Operationally Actionable Output
    • 9.1 Actionable Output
    • Applications
  • Chapter 10: Normal Crime
    • 10.1 Knowing Normal
    • 10.2 “Normal” Criminal Behavior
    • 10.3 Get to Know “Normal” Crime Trends and Patterns
    • 10.4 Staged Crime
  • Chapter 11: Behavioral Analysis of Violent Crime
    • 11.1 Case-Based Reasoning
    • 11.2 Homicide
    • 11.3 Strategic Characterization
    • 11.4 Automated Motive Determination
    • 11.5 Drug-Related Violence
    • 11.6 Aggravated Assault
    • 11.7 Sexual Assault
    • 11.8 Victimology
    • 11.9 Moving from Investigation to Prevention
  • Chapter 12: Risk and Threat Assessment
    • 12.1 Risk-Based Deployment
    • 12.2 Experts versus Expert Systems
    • 12.3 “Normal” Crime
    • 12.4 Surveillance Detection
    • 12.5 Strategic Characterization
    • 12.6 Vulnerable Locations
    • 12.7 Schools
    • 12.8 Data
    • 12.9 Accuracy versus Generalizability
    • 12.10 “Cost” Analysis
    • 12.11 Evaluation
    • 12.12 Output
    • 12.13 Novel Approaches to Risk and Threat Assessment
    • Case Examples
  • Chapter 13: Deployment
    • 13.1 Patrol Services
    • 13.2 Structuring Patrol Deployment
    • 13.3 Data
    • 13.4 How To
    • 13.5 Tactical Deployment
    • 13.6 Risk-Based Deployment Overview
    • 13.7 Operationally Actionable Output
    • 13.8 Risk-Based Deployment Case Studies7
  • Chapter 14: Surveillance Detection
    • 14.1 Surveillance Detection and Other Suspicious Situations
    • 14.2 Natural Surveillance
    • 14.3 Location, Location, Location
    • 14.4 More Complex Surveillance Detection
    • 14.5 Internet Surveillance Detection
    • 14.6 How To
    • 14.7 Summary
    • Advanced Concepts and Future Trends
  • Chapter 15: Advanced Topics
    • 15.1 Intrusion Detection
    • 15.2 Identify Theft
    • 15.3 Syndromic Surveillance
    • 15.4 Data Collection, Fusion and Preprocessing
    • 15.5 Text Mining
    • 15.6 Fraud Detection
    • 15.7 Consensus Opinions
    • 15.8 Expert Options
  • Chapter 16: Future Trends
    • 16.1 Text Mining
    • 16.2 Fusion Centers
    • 16.3 “Functional” Interoperability
    • 16.4 “Virtual” Warehouses
    • 16.5 Domain-Specific Tools
    • 16.6 Closing Thoughts
  • Index

Quotes and reviews

"[Data Mining and Predictive Analysis] is a must-read..., blending analytical horsepower with real-life operational examples. Operators owe it to themselves to dig in and make tactical decisions more efficiently, and learn the language that sells good tactics to leadership. Analysts, intell support, and leaders owe it to themselves to learn a new way to attack the problem in support of law enforcement, security, and intelligence operations. Not just a dilettante academic, Dr. McCue is passionate about getting the best tactical solution in the most efficient way-and she uses data mining to do it. Understandable yet detailed, [Data Mining and Predictive Analysis] puts forth a solid argument for integrating predictive analytics into action. Not just for analysts!"
- Tim King (Director, Special Programs and Global Business Development, ArmorGroup International Training)
 
 
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